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dc.contributor.authorArpogaus, Marcel-
dc.contributor.authorVoss, Marcus-
dc.contributor.authorSick, Beate-
dc.contributor.authorNigge-Uricher, Mark-
dc.contributor.authorDürr, Oliver-
dc.date.accessioned2023-04-06T13:09:23Z-
dc.date.available2023-04-06T13:09:23Z-
dc.date.issued2021-
dc.identifier.urihttps://www.climatechange.ai/papers/icml2021/20de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/27563-
dc.description.abstractThe transition to a fully renewable energy grid requires better forecasting of demand at the lowvoltage level to increase efficiency and ensure a reliable control. However, high fluctuations and increasing electrification cause huge forecast errors with traditional point estimates. Probabilistic load forecasts take future uncertainties into account and thus enables various applications in low-carbon energy systems. We propose an approach for flexible conditional density forecasting of short-term load based on Bernstein-Polynomial Normalizing Flows where a neural network controls the parameters of the flow. In an empirical study with 363 smart meter customers, our density predictions compare favorably against Gaussian and Gaussian mixture densities and also outperform a non-parametric approach based on the pinball loss for 24h-ahead load forecasting for two different neural network architectures.de_CH
dc.language.isoende_CH
dc.publisherICMLde_CH
dc.relation.ispartofInternational Conference on Machine Learning (ICML) Workshop Tackling Climate Change with Machine Learning, online, 26 June 2021de_CH
dc.rightsLicence according to publishing contractde_CH
dc.subjectDeep learningde_CH
dc.subjectForecastde_CH
dc.subjectEnergieversorgungde_CH
dc.subjectNetzstabilitätde_CH
dc.subject.ddc006: Spezielle Computerverfahrende_CH
dc.subject.ddc333.79: Energiede_CH
dc.titleProbabilistic short-term low-voltage load forecasting using bernstein-polynomial normalizing flowsde_CH
dc.typeKonferenz: Paperde_CH
dcterms.typeTextde_CH
zhaw.departementSchool of Engineeringde_CH
zhaw.organisationalunitInstitut für Datenanalyse und Prozessdesign (IDP)de_CH
zhaw.funding.euNode_CH
zhaw.originated.zhawYesde_CH
zhaw.publication.statuspublishedVersionde_CH
zhaw.publication.reviewPeer review (Publikation)de_CH
zhaw.author.additionalNode_CH
zhaw.display.portraitYesde_CH
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Arpogaus, M., Voss, M., Sick, B., Nigge-Uricher, M., & Dürr, O. (2021). Probabilistic short-term low-voltage load forecasting using bernstein-polynomial normalizing flows. International Conference on Machine Learning (ICML) Workshop Tackling Climate Change with Machine Learning, Online, 26 June 2021. https://www.climatechange.ai/papers/icml2021/20
Arpogaus, M. et al. (2021) ‘Probabilistic short-term low-voltage load forecasting using bernstein-polynomial normalizing flows’, in International Conference on Machine Learning (ICML) Workshop Tackling Climate Change with Machine Learning, online, 26 June 2021. ICML. Available at: https://www.climatechange.ai/papers/icml2021/20.
M. Arpogaus, M. Voss, B. Sick, M. Nigge-Uricher, and O. Dürr, “Probabilistic short-term low-voltage load forecasting using bernstein-polynomial normalizing flows,” in International Conference on Machine Learning (ICML) Workshop Tackling Climate Change with Machine Learning, online, 26 June 2021, 2021. [Online]. Available: https://www.climatechange.ai/papers/icml2021/20
ARPOGAUS, Marcel, Marcus VOSS, Beate SICK, Mark NIGGE-URICHER und Oliver DÜRR, 2021. Probabilistic short-term low-voltage load forecasting using bernstein-polynomial normalizing flows. In: International Conference on Machine Learning (ICML) Workshop Tackling Climate Change with Machine Learning, online, 26 June 2021 [online]. Conference paper. ICML. 2021. Verfügbar unter: https://www.climatechange.ai/papers/icml2021/20
Arpogaus, Marcel, Marcus Voss, Beate Sick, Mark Nigge-Uricher, and Oliver Dürr. 2021. “Probabilistic Short-Term Low-Voltage Load Forecasting Using Bernstein-Polynomial Normalizing Flows.” Conference paper. In International Conference on Machine Learning (ICML) Workshop Tackling Climate Change with Machine Learning, Online, 26 June 2021. ICML. https://www.climatechange.ai/papers/icml2021/20.
Arpogaus, Marcel, et al. “Probabilistic Short-Term Low-Voltage Load Forecasting Using Bernstein-Polynomial Normalizing Flows.” International Conference on Machine Learning (ICML) Workshop Tackling Climate Change with Machine Learning, Online, 26 June 2021, ICML, 2021, https://www.climatechange.ai/papers/icml2021/20.


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